TEC: PROS Espouses Its Artificial Intelligence (AI) Strategy to Power Modern Commerce

July 7th, 2017

July 7, 2017

By PJ Jakovljevic

PROS Inc. is a publicly traded (NYSE: PRO) cloud software company “powering the shift to modern commerce” by helping companies create personalized and effortless customer buying experiences. PROS’s latest software solutions are fueled by dynamic pricing optimization science; configure, price, quote (CPQ) and e-commerce software (the company’s original offerings); and machine learning capabilities. This software enables companies to price, configure, and sell their products and services in an omnichannel environment with speed, precision, and consistency.

PROS Outperform 2017 Conference Recap

PROS recently hosted guests from more than 300 companies, 39 countries, and 20 industries at its PROS Outperform 2017 conference in Chicago. Focused on the “Powering Modern Commerce” tagline, leaders from Cargill, HP, Honeywell, Hub Group, Land O’Lakes, Manitou America, McKesson Medical-Surgical, Southwest Airlines, TAP Portugal, and many others shared their firsthand experiences in leveraging PROS solutions to outperform competitors in the era of modern commerce.

During a keynote by PROS CEO Andres Reiner and VP of Global Sales for Microsoft Dynamics Hayden Stafford, PROS unveiled its latest innovations in machine learning algorithms and the ability of these algorithms to uncover hidden sales opportunities, improve demand forecasting around special events, and deliver richer personalization for e-commerce buyers. The keynote also featured modern commerce experiences using Microsoft HoloLens, which configured products in virtual reality, and included a real-time demonstration of Saint-Gobain Glass Solution’s self-serve app for configuring and ordering glass products from a phone, tablet, or computer.

We interviewed Justin Silver, Ph.D., Senior Scientist from the PROS Science & Research team, on the company’s AI strategy for modern commerce. Justin joined PROS in 2013 and a Ph.D. in Statistics, an M.A. in Statistics, and B.A. in Mathematics, Economics, and Computational Finance, all from Rice University.

TEC: Do you agree with the simplified definitions of AI, machine learning, cognitive, deep learning, next-best-action, and other related concepts in our AI Report?

JS: I agree that “AI” is the umbrella encompassing the other terms, but at the highest level AI is the category of capabilities that essentially allows machines to think like humans. I would also add that cognitive systems offer many defining elements, one of which is machine learning, with deep learning as a subset. I also agree that these definitions are open to debate and can have different meanings in different contexts.

At PROS, we use multiple flavors of data science: demand forecasting, optimization, statistical segmentation, collaborative filtering, probabilistic modeling, natural language processing (NLP), and many more. These techniques cross a number of technical disciplines, including machine learning and cognitive computing. This reflects the varied expertise of our Science & Research team.

TEC: What is your AI technology based on?

JS: Over more than three decades, our machine learning models have been trained intensively to understand deeply the domains we serve. This gives our systems visibility where an untrained, off-the-shelf model would likely have blind spots. For example, in some of our applications we use a Bayesian hierarchical model to forecast demand, which requires tuning a large number of parameters and hyper-parameters. Our vast experience with implementing this model for our customers, as well as the large volume of data the model has been trained on over time, has enabled us to deliver accurate forecasts to our customers as soon as they turn on the PROS system.

AI has rapidly evolved over the last 50 years, with constant advancements from thousands of companies, universities, and individuals. However, many times we’ve needed to build extensions or customized versions of algorithms from existing libraries. Our partnership with Microsoft and the Azure platform enable our innovation and accelerate our speed to market. We also recently announced a partnership with CognitiveScale, a provider of industry-specific augmented intelligence software. This partnership will help us enhance our product offerings with increased personalization capabilities.

Concrete PROS AI Solutions

TEC: Could you provide a concrete example of what your AI tools can do for the customer?

JS: The common thread throughout all our offerings is using predictive science and optimization techniques to enable our customers to provide personalized and frictionless customer experiences. We use several types of predictive science, including forecasting, pattern recognition, reinforcement learning, anomaly detection, and supervised and unsupervised learning.

We help companies by providing guidance on customer-specific pricing decisions, which include a projection of a customer’s willingness-to-pay based on their respective customer segment attributes. Our Guidance product uses supervised machine learning to provide dynamic price recommendations based on insights from massive amounts of transactional history, supplemented by external buying signals (see figures below).Figure 1. Screenshot of PROS Guidance showing price recommendations based on specific customers and products

Another example would be the set of algorithms that help our customers identify high-probability sales opportunities inside their own customer bases. Our Opportunity Detection solution (see Figure 3) leverages unsupervised learning techniques to identify groups of customers with similar buying behaviors. It also works in conjunction with supervised learning techniques to identify customer purchase patterns.

Figure 3. Screenshot of PROS Guidance showing opportunity detection

The ultimate output is prescriptive recommendations that align with buyer expectations in terms of price, product, offer, and speed, which result in more wins, greater profitability, and happier customers. These are just a few of the many ways we use AI methods to drive value for our customers.

JS: We’re already seeing an increase in interest from business to business (B2B) companies looking for a CPQ solution that enables a consistent, frictionless, and personalized buying experience across direct, partner, and self-serve e-commerce channels. We anticipate that interest to grow. AI plays a pivotal role in harnessing the connectedness of all the necessary data, which exists in a variety of raw forms, including structured tables, text, images, and others.

With a growing number of B2B transactions moving to e-commerce, we see dynamic pricing as even more crucial for delivering a frictionless experience. In this environment, sales representatives lose the opportunity to negotiate price or even make the case for a price. Real-time dynamic pricing—powered by AI—encourages buyers to accept the first price they see. It also ensures that prices are accurate and up to date across channels. For one of our customers, dynamic pricing reduced their quote turnaround time by 60%, increased their win rates, and resulted in more than $250 million in incremental revenue.

Personalization is another sharply growing trend. Buyers now expect their experiences to be more relevant and personal. We deliver personalization through one-click Fast Configuration in our Smart CPQ solution. Fast Configuration is an accelerated guided-selling feature powered by AI. By leveraging purchase-pattern data, Fast Configuration recommends a product specifically configured to meet the most likely needs of the customer, without having to ask all the questions commonly used in rules-based guided selling.

In addition, Fast Configuration includes recommendations on price, cross-sell, and even substitute options. The AI embedded in Fast Configuration and applied to this process continues to learn new pathways. As users use the system, the feature even modifies existing pathways, which results in better recommendations over time.

We also use cognitive computing to deliver richer personalization. In this case, the cognitive system serves up only the options most closely aligned with the buyer’s purchase history, digital exhaust, and interactions with the system. AI is becoming more accepted across many different contexts. We believe this will continue to fuel the number of uses for AI and help our customers accomplish their growth and profitability goals.

TEC: Do you think prospects are leery of AI?

JS: Across the board, we’re seeing a much greater acceptance of AI as a viable and critical tool for businesses, and we anticipate even greater interest as these “intelligent software” requirements continue to emerge quickly.

The most common concern seems to be how and where AI should be applied in companies. We share a broad range of use cases from others who have applied AI to their commerce strategies—revenue management, pricing, selling, ecommerce—to help companies overcome these fears.

Seeing and measuring the impact of AI on commerce gives companies greater confidence to apply AI elsewhere. We’ve also found it most beneficial to stay focused on how our AI tools and techniques pertain to the customer’s business objectives: What problem(s) are they trying to solve? What data is already available? What data could be gathered that would be helpful? Given this data, how can we deliver a solution that will provide ongoing value and adapt according to changes in the market, business needs, and user preferences?

The marriage of business acumen and AI takes commitment, which PROS customers understand and embrace. There are cases where the system can make decisions with confidence and others where input from subject-matter experts can provide additional support. The outcomes of those human-driven cases are then used to train the system further. We work with customers to clearly define what success looks like, which is crucial to our joint success. To implement an AI solution, we track key performance indicators (KPI’s) to make sure the system is moving down the right path and we measure the results.

TEC looks forward to seeing what other AI solutions PROS will develop to help companies sift through their big data from sales and make more informed and intelligent decisions.